Extended Kalman Filter for State Estimation and Trajectory Prediction of a Moving Object Detected by an Unmanned Aerial Vehicle

Prévost, Carole Gabrielle; Desbiens, André; Gagnon, Éric

doi:10.1109/acc.2007.4282823

Cited by 97 publications

(52 citation statements)

References 6 publications

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“…The Kalman filter and its extension have been proved appropriate for trajectory analysis. Recent works include Prevost et al (2007), which presents an extended Kalman filter to predict the trajectory of a moving object based on the measurement data from a moving sensor -an unmanned aerial system (UAS). An unscented Kalman filter is used in Sun et al (2012) for trajectory tracking based on satellite data with weak observability and inherent large initial error.…”

Section: Introductionmentioning

confidence: 99%

Anomalous behavior detection in single-trajectory data

Huang

2015

International Journal of Geographical Information Science

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This paper presents an original approach to dynamic anomalous behavior detection in individual trajectory using a recursive Bayesian filter. The anomalous pattern detection is of great interest for navigation, driver assistance systems, surveillance as well as crisis management. In this work, we focus on the GPS trajectories of automobiles finding where the driver's behavior shows anomalies. Such anomalous behaviors can happen in many cases, especially when the driver encounters orientation problems, i.e., taking a wrong turn, performing a detour, or losing the way. First, three high-level features, i.e., turns and their density, detour factor, and route repetition are extracted from the given trajectory geometry, for which a long-term perspective is required to observe data sequences of a significant length instead of individual time stamps. We therefore employ high-order Markov chains with a 'dynamic memory' to model the trajectory integrating these long-term features. The Markov model is processed by a proposed recursive Bayesian filter to infer an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The filter performs unsupervised detection in single trajectories based on local features only. No training process is required to characterize the anomalous behaviors. By analyzing the detection results of individual trajectories, collective behaviors can be derived indicating traffic issues such as congestions and turn restrictions. Experiments are performed on volunteered geographic information (VGI) data, self-acquired trajectories, and open trajectory datasets to demonstrate the potential of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Anomalous behavior detection in single-trajectory data

Huang

2015

International Journal of Geographical Information Science

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“…To perform these estimates, Kalman Filters are commonly used to estimate future states based upon previous observed states [7], [8]. For the most part, they assume a geospatial 2-D Gaussian distribution with monolithic variances in both the normal and tangential directions of motion [8].…”

Section: Introductionmentioning

confidence: 99%

First-Order Short-Range Mover Prediction Model (SRMPM)

Overstreet

Khorrami

2011

Proceedings of the 2011 American Control Conference

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An important problem for intelligent autonomous mobile systems/agents is the ability to predict the motions of other objects/agents. This has natural extensions to cooperative behavior control, where mobile agents avoid each other by predicting the other's motion. In this paper, we have formulated a spatial probability distribution for moving objects with respect to First-Order predictions, which take into account mobility characteristics and how they relate to probable motion. This is a novel method since the most common approach uses Kalman Filters to estimate future states based upon observed previous states only, assuming a geospatial 2-D Gaussian distribution with monolithic variances in both the normal and tangential directions of motion. Unlike prior approaches, our methodology takes into consideration specific dynamic constraints (e.g., Ackermann Steering), and probable decision making capabilities of the mover. By adding higher levels of fidelity to prediction models, more accurate and precise object tracking, avoidance, or engagement can be accomplished with already developed techniques.

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“…Observer-based control for visual servo control of UAV has been proposed for example in [4] using image provided by a camera for the estimation of the velocity. Extended Kalman Filter is used in [8] to estimate the state of a moving object detected by a UAV. The disadvantage of this method is its only local convergence.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous observer based sensor diagnosis and speed estimation of Unmanned Aerial Vehicle

Rafaralahy

Richard

Boutayeb

et al. 2008

2008 47th IEEE Conference on Decision and Control

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In this contribution we investigate the problem of simultaneous observer based sensor diagnosis and speed estimation of Unmanned Aerial Vehicle (UAV). The main features lie in the use of a useful bank of reduced order observers to detect and isolate faulty sensors and in the same time to provide unbiased speed estimation of UAV from accelerometers. From a structural analysis, we provide all trajectories for which faults would be detected and estimated. The theoretical result is summarized into the stability analysis, established through the Lyapunov approach and Barbalat lemma. The existence condition is expressed in terms of physical variables of the UAV. Performances and easiness of implementation of the proposed technique are shown through a quadrotor UAV.

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Extended Kalman Filter for State Estimation and Trajectory Prediction of a Moving Object Detected by an Unmanned Aerial Vehicle

Cited by 97 publications

References 6 publications

Anomalous behavior detection in single-trajectory data

Anomalous behavior detection in single-trajectory data

First-Order Short-Range Mover Prediction Model (SRMPM)

Simultaneous observer based sensor diagnosis and speed estimation of Unmanned Aerial Vehicle

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